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A Critique and Improvement of an Evaluation Metric for Text Segmentation
Open accessAbstract
The P k evaluation metric, initially proposed by Beeferman, Berger, and Lafferty (1997), is becoming the standard measure for assessing text segmentation algorithms. However, a theoretical analysis of the metric finds several problems: the metric penalizes false negatives more heavily than false positives, overpenalizes near misses, and is affected by variation in segment size distribution. We propose a simple modification to the P k metric that remedies these problems. This new metric—called Window Diff—moves a fixed-sized window across the text and penalizes the algorithm whenever the number of boundaries within the window does not match the true number of boundaries for that window of text.
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Cited by
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Artstein, R., & Poesio, M. (2008) · Computational Linguistics · doi:10.1162/coli.07-034-r2 · Cited by 1,665
Discourse Segmentation of Multi-Party Conversation
Galley, M., McKeown, K., Fosler-Lussier, E., & Jing, H. (2003) · Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - ACL '03 · doi:10.3115/1075096.1075167 · Cited by 351
Text Segmentation by Cross Segment Attention
Lukasik, M., Dadachev, B., Papineni, K., & Simões, G. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.380 · Cited by 68
Using Deep-Learned Vector Representations for Page Stream Segmentation by Agglomerative Clustering
Busch, L., van Heusden, R., & Marx, M. (2023) · Algorithms · doi:10.3390/a16050259 · Cited by 1
Metadata
- Authors
- Pevzner, L., & Hearst, M. A.
- Venue
- Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2002-03
- Volume
- 28
- Issue
- 1
- Pages
- 19-36
- ISSN
- 0891-2017 (ISSN-L)
- 1530-9312
- OpenAlex
- W2159083595
- Cited by
- 440
- References
- 0
- FWCI
- 6.69
- Type
- journal article
- Language
- en
- Open access
- bronze
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